Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.

@article{citeulike:86948, abstract = {We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70-80 percent of the spikes in the full model (with temporal precision of +/-2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.}, address = {Laboratory of Computational Neuroscience, Swiss Federal Institute of Technology, Ecole Polytechnique F\'{e}d\'{e}rale de Lausanne, 1015 Lausanne, Switzerland. renaud.jolivet@epfl.ch}, author = {Jolivet, R. and Lewis, T. J. and Gerstner, W. }, citeulike-article-id = {86948}, doi = {http://dx.doi.org/10.1152/jn.00190.2004}, issn = {0022-3077}, journal = {J Neurophysiol}, keywords = {dynamics, integrate-and-fire, networks, neural}, month = {August}, number = {2}, pages = {959--976}, posted-at = {2008-06-11 16:11:33}, priority = {3}, title = {Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.}, url = {http://dx.doi.org/10.1152/jn.00190.2004}, volume = {92}, year = {2004} }

TY - JOUR ID - citeulike:86948 L3 - citeulike-article-id:86948 N2 - We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70-80 percent of the spikes in the full model (with temporal precision of +/-2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons. IS - 2 JF - J Neurophysiol SN - 0022-3077 AD - Laboratory of Computational Neuroscience, Swiss Federal Institute of Technology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. renaud.jolivet@epfl.ch EP - 976 TI - Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. VL - 92 SP - 959 KW - dynamics KW - integrate-and-fire KW - networks KW - neural AU - Jolivet, R AU - Lewis, TJ AU - Gerstner, W PY - 2004/08// UR - http://dx.doi.org/10.1152/jn.00190.2004 ER -